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A large-scale immuno-epidemiological simulation of influenza A epidemics

Lukens, S and Depasse, J and Rosenfeld, R and Ghedin, E and Mochan, E and Brown, ST and Grefenstette, J and Burke, DS and Swigon, D and Clermont, G (2014) A large-scale immuno-epidemiological simulation of influenza A epidemics. BMC Public Health, 14 (1).

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© 2014Lukens et al.; licensee BioMed Central Ltd. Background: Agent based models (ABM) are useful to explore population-level scenarios of disease spread and containment, but typically characterize infected individuals using simplified models of infection and symptoms dynamics. Adding more realistic models of individual infections and symptoms may help to create more realistic population level epidemic dynamics.Methods. Using an equation-based, host-level mathematical model of influenza A virus infection, we develop a function that expresses the dependence of infectivity and symptoms of an infected individual on initial viral load, age, and viral strain phenotype. We incorporate this response function in a population-scale agent-based model of influenza A epidemic to create a hybrid multiscale modeling framework that reflects both population dynamics and individualized host response to infection.Results: At the host level, we estimate parameter ranges using experimental data of H1N1 viral titers and symptoms measured in humans. By linearization of symptoms responses of the host-level model we obtain a map of the parameters of the model that characterizes clinical phenotypes of influenza infection and immune response variability over the population. At the population-level model, we analyze the effect of individualizing viral response in agent-based model by simulating epidemics across Allegheny County, Pennsylvania under both age-specific and age-independent severity assumptions.Conclusions: We present a framework for multi-scale simulations of influenza epidemics that enables the study of population-level effects of individual differences in infections and symptoms, with minimal additional computational cost compared to the existing population-level simulations.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Lukens, S
Depasse, J
Rosenfeld, R
Ghedin, Eelg21@pitt.eduELG21
Mochan, E
Brown, ST
Grefenstette, Jgref@pitt.eduGREF
Burke, DSdonburke@pitt.eduDONBURKE
Swigon, Dswigon@pitt.eduSWIGON
Clermont, Gcler@pitt.eduCLER
Centers: Other Centers, Institutes, Offices, or Units > Center for Vaccine Research
Date: 29 September 2014
Date Type: Publication
Journal or Publication Title: BMC Public Health
Volume: 14
Number: 1
DOI or Unique Handle: 10.1186/1471-2458-14-1019
Schools and Programs: Dietrich School of Arts and Sciences > Mathematics
Graduate School of Public Health > Biostatistics
Graduate School of Public Health > Epidemiology
School of Medicine > Computational and Systems Biology
School of Medicine > Critical Care Medicine
Refereed: Yes
Date Deposited: 05 May 2015 16:57
Last Modified: 02 Feb 2019 16:57


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